Learny AI

Learny AI is now a website with an animated HTML/CSS/JS frontend and a Python server backend.

The Python backend still keeps the original JSON brain:

User asks in the browser
-> Python server checks data/knowledge.json
-> If found, Learny answers from JSON
-> If not found and GROQ_API_KEY is set, Learny calls Groq
-> Tries llama-3.1-8b-instant first
-> Tries openai/gpt-oss-20b if the first model fails
-> Saves learned answers into data/knowledge.json
-> If both models fail, returns the unknown message

Run The Website With Wasmer

wasmer run . --net --env PORT=8000

Open:

http://127.0.0.1:8000

Do not launch web/index.html directly for normal use. The page can display from file://, but the chat needs the Wasmer-hosted Python API for the JSON brain and Groq learning.

Use another port:

wasmer run . --net --env PORT=8080

Run without Groq calls:

wasmer run . --net --env PORT=8000 --env GROQ_API_KEY=

Run Directly With Python

This is only a faster local development shortcut. Wasmer uses the same Python server through src/main.py.

python server.py

Open:

http://127.0.0.1:8000

Use another Python-only port:

python server.py --port 8080

Run Python directly without Groq calls:

python server.py --offline

Enable Groq Learning

Create a Groq API key at groq.com, then set it before starting the server:

$env:GROQ_API_KEY = "your-key-here"
wasmer run . --net --env PORT=8000 --env GROQ_API_KEY=$env:GROQ_API_KEY

The browser never receives your key. Only the Python backend reads GROQ_API_KEY.

Wasmer Deployment

This repo includes:

wasmer.toml
app.yaml
src/main.py

The Wasmer entrypoint is src/main.py, which runs the same Python server as server.py.

The Wasmer package namespace is set to adamsrealm1:

wasmer.toml -> [package].name
app.yaml -> package

Then deploy from the repo root:

wasmer deploy --publish-package

For local Wasmer testing:

wasmer run . --net --env PORT=8000

Teach Learny Manually

Edit data/knowledge.json.

Example:

{
  "questions": {
    "hello": [
      "Hello.",
      "Hi."
    ],
    "what is your name": "My name is Learny."
  }
}

Rules:

• Keep valid JSON. JSON does not allow comments.
• Put question phrases inside the questions object.
• Each question can have one answer as a string.
• Each question can also have multiple answers as a list of strings.
• Empty questions and empty answers are rejected.
• If several questions match, Learny uses the longest matching question.

Project Structure

Learny AI/
  app.yaml                 # Wasmer app deployment config
  server.py                # Local web server launcher
  wasmer.toml              # Wasmer package config
  data/
    knowledge.json         # Manual and learned questions/answers
  learny/
    bot.py                 # JSON-first answer flow
    cli.py                 # Original command line app
    conversation.py        # In-memory follow-up context
    groq_client.py         # Standard-library Groq API client
    knowledge.py           # JSON loading and validation
    memory.py              # Writes learned answers back to JSON
    text.py                # Text cleanup and phrase matching
    web_server.py          # Python HTTP server and API routes
  src/
    main.py                # Wasmer entrypoint
  tests/
    test_learny.py         # Core Learny tests
    test_web_server.py     # Website API tests
  web/
    index.html             # Website markup
    styles.css             # Animated polished UI
    app.js                 # Browser API calls and chat behavior
    assets/
      learny-core.png      # Generated bitmap UI asset

API Routes

GET  /api/status
GET  /api/knowledge
POST /api/ask

POST /api/ask accepts:

{
  "message": "hello",
  "sessionId": "optional-browser-session-id"
}

The server keeps follow-up context in memory per browser session.

Tests

python -m unittest discover -s tests -v